Lately, I’ve found myself thinking about the incredible promise of technology and how it often contrasts sharply with its real-world impact. As a communication student diving into global political economy and digital labor, I’ve come across some striking examples of how AI and tech giants are reshaping inequality and how this gap keeps growing.
Artificial intelligence is often portrayed as the future—a technology that will transform economies, enhance productivity, and create a new era of prosperity. However, beneath the glossy surface of AI and its promise of progress, there’s a deeper, more troubling reality. What if AI isn’t just an advanced tool of innovation but a machine built on invisible labor, inequality, and monopolistic control?
Artificial intelligence is often hailed as a revolutionary tool that will democratize opportunity, streamline services, and solve age-old problems. But who truly benefits from this innovation?
At the heart of AI’s so-called “intelligence” lies a critical, often overlooked truth: much of it is not “artificial” at all. It is powered by invisible, low-waged human labor. This phenomenon, known as “ghost work” (Ekbia & Nardi, 2017), is where real people—often in low-income countries—perform tasks that allow AI systems to function but are obscured by the glamour of technological advancement. Whether it’s content moderation, image recognition, or tagging data, these tasks are integral to AI systems but are typically not credited as part of the development of these technologies (Gray & Suri, 2019). This is the core of the illusion: AI is often marketed as a fully autonomous system, when in reality, it is a hybrid of human labor and technology.
The analogy to Kempelen’s Mechanical Turk, a 19th-century automaton that deceived people into thinking it was a self-operating machine, is striking. Much like the Turk, many of the “intelligent” systems we rely on today are powered by human effort, which is hidden behind a carefully constructed facade of technological prowess (Mozur, 2018). This illusion is crucial for maintaining the narrative of technological progress and reinforcing the belief that these systems are the product of advanced machine learning rather than the result of human work. This situation allows major corporations to outsource labor to countries with low wages, creating a cycle of exploitation that reinforces global inequalities.
As much as AI is driven by technological advances, it is also fueled by ideology and hype. The development of AI has been as much about cultural narratives and financial speculation as it has been about actual progress. By presenting AI as a self-sufficient, autonomous system, tech companies mask the underlying truth: these systems depend on massive investments, often from public sources, that promise future breakthroughs that may never materialize. This creates an ecosystem where financial speculation propels technological development, leading to rapid growth and an equally rapid crash when expectations are unmet (Sadowski & Bendor, 2019).
The ideological underpinnings of AI also serve to normalize exploitation. By framing AI as a solution to problems such as unemployment or inequality, the technology is presented as a form of progress, even when it is used to rationalize labor exploitation. This process of normalizing exploitation is integral to the system and it allows tech companies to continue reaping massive profits while workers remain invisible and underpaid.
Many governments lack the technical expertise or resources necessary to regulate AI effectively, resulting in a regulatory void that enables tech giants to further consolidate their power. While there have been efforts to regulate AI, such as the EU’s attempts to control data practices, these measures have proven insufficient. Data regulation, though important, is not the sole issue at play and has turned out to be less impactful than anticipated. Today, factors like labor and computational power have become even more critical in shaping the power dynamics of AI (Graham & Ferrari, 2022), yet these areas are often overlooked in current regulatory frameworks.
The rise of AI has definitely revealed a stark reflection of global inequalities, especially when we examine the geographies of digital labor. Digital labor markets often mirror colonial structures, where countries in the Global South provide the low-cost labor that fuels the global tech economy. Workers in the Philippines, India, and other outsourcing hubs are the ones carrying out the tedious, often low-paid tasks that make AI systems function (…). This arrangement is symptomatic of a larger, colonial dynamic where labor from the “margins” supports the wealth of the “core” nations (Graham & Anwar, 2019). This dynamic is strikingly neo-colonial in nature, perpetuating a reliance on undervalued labor from regions that historically bore the brunt of extractive economies.
At the same time, this pattern of exploitation isn’t confined to low-income nations. Workers in the so-called “core” nations are increasingly facing precarity as well. The gig economy, fueled by platforms like Uber, Amazon Mechanical Turk, and others, creates a landscape where both high-wage workers in wealthy countries and low-wage workers in the Global South share the experience of digital precarity. This growing inequality is fueled by tech companies that rely on these hybrid labor systems, where workers are treated as disposable and their contributions are erased behind the illusion of autonomous technology. This dual exploitation underscores a broader issue: the systemic undervaluation of human labor across all contexts in favor of dehumanized, market-driven narratives.
Even within the Global North, small businesses and non-profits struggle to compete with corporations armed with advanced tools and endless resources. The promise of technology as an equalizer feels increasingly hollow when access is determined by wealth and power. And as these disparities grow, so too does the chasm between those who shape our digital future and those who must simply adapt to it.
AI’s development relies heavily on massive cloud infrastructures controlled by tech giants. The narrative of AI as a universal good obscures the reality that its development and deployment are overwhelmingly controlled by a handful of corporations. These firms leverage vast computational infrastructures—cloud platforms that are essential to AI training and deployment—to consolidate profits.
The rise of AI is not just a story of technological innovation but also one of monopoly and control. The political economy of AI is driven by a cycle of speculation and investment, where tech companies hype their AI capabilities to attract funding and further solidify their dominance (Sadowski & Bendor, 2019). The AI market is today dominated by a handful of corporations mainly from two countries, the US and China — such as Amazon, Google, Tencent, and Alibaba—that control not just the technology but also the infrastructure required for it, such as cloud computing and data storage.
When thinking about the dominance of these companies, the word “monopoly” often comes to mind. These corporations wield immense power, controlling markets and setting terms for everyone else. Yet, economist Yanis Varoufakis describes their rise not as mere monopolies but as part of a broader shift towards “technofeudalism” (Varoufakis, 2024). This term suggests a move beyond traditional capitalism—where wealth derived from the sale of goods or services—toward a system where owning clouding platforms becomes the source of power. He also refers to it as “cloud capitalism”. Instead of competing to create, these giants extract value by controlling access to digital spaces. They have created a system where power is not only concentrated but also becomes self-reinforcing.
In this system, the monopolistic behavior of these tech giants leads to the accumulation of capital and data, which they use to further entrench their dominance. Technofeudalism, as Varoufakis describes it, arises when these giants create a global economy where they control the digital infrastructure, and workers, consumers, and smaller companies are all dependent on them (Varoufakis, 2024). This results in a new form of feudalism, where tech companies act as digital overlords, holding sway over the global economy through their control of data, technology, and, crucially, labor. It’s a system reminiscent of industrial revolutions past: those who own the tools prosper, while the rest struggle to keep up.
In such a scenario, the dream of technological democratization seems increasingly out of reach, and the question remains: how do we reclaim agency in an age dominated by digital overlords?
References:
Ekbia, Hamid R., and Bonnie A. Nardi. 2017. Heteromation, and Other Stories of Computing and Capitalism. Cambridge, MA: MIT Press.
Graham, M., Ferrari, F. (2022). Digital work in the planetary market. Cambridge: The MIT Press.
Gray, Mary L., and Siddharth Suri. 2019. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Boston: Houghton Mifflin Harcourt.
Paul. 2018. “Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras.” New York
Times, July 8. https://www.nytimes.com/2018/07/08/business/china-surveillance-technology.html
Sadowski, Jathan, and Roy Bendor. 2019. “Selling Smartness: Corporate Narratives and the Smart City as a Sociotechnical Imaginary.” Science, Technology, & Human Values 44 (3).
Varoufakis, Y. (2024). Technofeudalism: what killed capitalism. Brooklyn, NY, Melville House.
Von Laufenberg, R. (2022, February 10). The Mechanical Turk – or the invisible, low-cost labour of automation — VICESSE. VICESSE. https://vicesse.eu/blog/2022/2/8/the-mechanical-turk-or-the-invisible-low-cost-labour-of-automation